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How to implement self-service analytics


Self-service analytics
Credit: clicdata

Picture this: Your data team, a group of dedicated and highly skilled professionals, is anchored at the heart of the organization. Once the unsung heroes who methodically churned out reports from behind the scenes, they now find themselves thrust into the limelight. Every department, from marketing to finance, turns to them with hungry eyes, seeking data-driven answers for an array of questions. "How did our recent campaign fare?" "Can we optimize our operational costs?" "What’s the churn rate for the last quarter?" The list is endless.


And herein lies the pinch. Your team, as efficient and dedicated as they are, is constantly drowning in a deluge of requests. Each morning, as they fire up their systems, a barrage of emails, slack messages, and the classic sticky note on the monitor reminds them of the growing backlog. The once-clear distinction between primary tasks and ad-hoc requests blurs, leading to longer turnaround times, occasional miscommunications, suboptimal optimisation, and an underlying sense of being perpetually behind schedule.


It's not that the team isn't capable. On the contrary, they're wizards with data. But the sheer volume and diversity of requests have created bottlenecks that no amount of midnight oil can clear. Moreover, the traditional role of data teams has evolved. Gone are the days when they were just gatekeepers of the data realm. Today, they're expected to be enablers, collaborators, and even educators—helping every single person in the organization harness the power of data.


Yet, without a structured, scalable solution, these issues will only magnify. We've seen it before: medium sized organisations with 20+ data experts. The ad-hoc nature of requests leads to a lack of standardized processes. Reliance on specific team members for particular datasets creates unnecessary dependencies. And all these challenges culminate in one overarching problem: The growing gap between the organization’s thirst for insights and the data team’s ability to quench it.


But, every challenge paves the way for innovation. The way you handle this problem will shape the culture and effectiveness of the organisation for years to come.


The Crossroads: Which Path to Choose?

A quick fix can be tempting. After all, you're yearning for a reprieve from the incessant barrage of data demands. Let's dissect the traditional paths that might seem inviting:


1. Expand the Data Team:

The logic is straightforward: If the workload is too much for the current team, why not bring more hands on deck? More analysts and data professionals to tackle the avalanche of requests.

Pros:

  • With additional members, specialized tasks can be divided, and expertise can be diversified.

  • Immediate relief to the existing team, as new members absorb some of the overload.

Cons:

  • The addition of personnel is a costly affair, both in terms of recruitment and ongoing salaries.

  • Additional staff means additional management, co-ordination and communication time, slowing the whole team down

  • Simply expanding the team doesn't address the root issue. The volume of requests will inevitably increase as the organization grows, leading you back to the same bottleneck.


2. Rely on Traditional BI Tools:

Many organizations, maybe even yours, have flirted with the idea of using ready-made Business Intelligence solutions. These tools, with their glossy interfaces and bold promises, often suggest that they are the magic bullet for all analytical woes.

Pros:

  • Immediate access to pre-built dashboards and standardized reports.

  • A perceived sense of order and structure in handling data requests.

Cons:

  • These tools often come with steep learning curves. Your non-technical departments might still need hand-holding, which defeats the purpose.

  • They can be rigid. While they offer a plethora of features, customizing them to suit specific organizational needs can be an uphill battle. Not to mention, the hidden costs associated with licenses, integrations, and maintenance.

  • They rarely solve all the problems for each department, and are quickly outgrown with niche analysis requirements


3. Locking Down Data Access:

In the face of potential misuse or misinterpretation, the idea of limiting data access might seem prudent. Centralizing decisions can ensure data integrity, right?

Pros:

  • Centralized data means there's less risk of disparate interpretations.

  • Ensures that sensitive data remains in safe hands.

Cons:

  • Inhibits organizational agility. With limited access, departments can't make real-time, data-driven decisions.

  • Can create a chasm between the data team and other departments, breeding mistrust. After all, data shouldn't be a privilege but a shared resource.

As you mull over these options, it's essential to challenge the traditional mindset. The prevalent notion that problems of scale can be solved merely by throwing more resources or tools at them is a mirage. Opting for such solutions might bring temporary respite, but sooner or later, the cracks will begin to show, and you'll find yourself at yet another crossroads.

But what if there was another path? One that doesn't just tackle the current challenges but reshapes the very fabric of how your organization interacts with data?


Self-Service Analytics: Embrace, Educate, Empower


Embrace

In this initial phase, it's all about mindset. The data, the tools, the processes – they’re vital, yes, but the real transformation begins with acceptance. Acknowledging that with the right environment, everyone can (and should) be a data analyst in their own right. Don't hoard that power, it's meant for everyone.

  • Action Steps:

    • Vet and select a self-service analytics platform that aligns with your organization's needs.

    • Establish robust data governance policies to maintain data quality and integrity.

    • Define clear user roles and permissions, ensuring data access is both democratised and secure.


People often ask us, "How do you start this journey?" And our answer is always the same: start by fostering a culture that values data not just as a tool, but as a vital organizational asset.


Educate

As you lay the foundation with the right tools and policies, the next step is to illuminate the path for others. Equip them not just with the skills to use these tools, but imbue them with the mindset of a data enthusiast. Yes, this is hard.

  • Action Steps:

    • Host regular workshops tailored for different expertise levels, ensuring everyone from a newbie to a data aficionado finds value.

    • Create comprehensive documentation, a repository that serves as the first point of reference for any data-related queries.

    • Offer one-on-one mentorship sessions, ensuring personalized guidance and fostering a deeper understanding.


We've helped many organizations in this phase, and the results often echo a similar sentiment: "Our teams are energised to answer all the questions they've always had but couldn't find answers to."


Empower

The transformation. As departments begin to autonomously tap into insights, you'll see a ripple effect. Decisions become swifter, innovations flourish, and a once data-hesitant organization transforms into a data-forward powerhouse. The data team will start receiving less specific ad hoc data requests but more requests to share advanced methods and teach best practice.

  • Action Steps:

    • Encourage departments to showcase their data discoveries, fostering a culture of knowledge sharing.

    • Set up a feedback loop, where users can report challenges, suggest improvements, and ensure the system is continually refined.

    • Start capturing success stories from around the organisation and sharing them

    • Celebrate successes, big or small. Every insight gleaned, every data-driven decision made, marks a step forward in this collective journey.


Some organisations have succeeded in this and become industry giants. Others have stopped halfway, unable to overcome the ego-driven acceptance of letting the business users perform their own analysis and not being heavily depended on. And you now have the option and knowledge how to make this change and empower your organisation for years to come.


All these changes rely on having the right systems that enable self-service analytics. We've built our data platform so it can be operated by analysts and business users. Check out our features page to see more.

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